Improved epidemic monitoring using sewage

Led by the DTU National Food Institute, scientists from 11 European universities, institutions and scientific organizations have developed a new method for analyzing wastewater monitoring data. The method can help determine whether pathogenic bacteria, viruses and antimicrobial resistance originate from humans, animals, industry or the environment. Potentially thousands of threats can be detected simultaneously, including antimicrobial resistance and cholera bacteria, which can help prevent disease outbreaks from escalating into epidemics. The research has been published in the scientific journal Nature communication.

Scientists analysed samples taken over three years from seven wastewater treatment plants in five major European cities: Bologna, Budapest, Copenhagen, Rome and Rotterdam.

“Untreated sewage is becoming an increasingly important source of anonymous health and disease surveillance in large urban populations. However, extracting valuable data from it is not easy, because sewage contains both known and unknown bacteria from various sources, such as humans, plants, animals, rainwater, dishwashing, etc.,” says the corresponding author of the research paper, Assistant Professor Patrick Munk from the DTU National Food Institute.

Furthermore, the composition of wastewater may change due to seasonal changes in temperature.

Scientists are beginning to overcome these difficulties using a new computer program.

“Our study shows significant potential in metagenomics-based wastewater monitoring. Although this method is more expensive than the PCR test, which has proven to be highly effective during the COVID-19 pandemic, PCR only screens for one hazard at a time. Metagenomics-based wastewater monitoring can assess thousands of hazards simultaneously. Furthermore, the value of each individual sample increases with the number of samples collected over time, as historical data increases the value of new analyses,” says Professor Frank Aarestrup, who heads the Genetic Epidemiology Research Group at the DTU National Food Institute and is a co-author of the paper.

One could consider a monitoring system that combines metagenomics-based wastewater surveillance with PCR testing to detect specific threats that authorities consider likely to occur.

The study is particularly important because an EU directive requires all major European cities to start monitoring AMR in wastewater. In Denmark, Statens Serum Institut is leading a large European collaboration on implementing wastewater monitoring.

The software organizes huge data sets into mysterious groups

Over a three-year period, from January 2019 to November 2021, 278 wastewater samples were collected from the inlets of seven wastewater treatment plants and sent to DTU. The researchers then analyzed billions of DNA sequences from the samples, assembling them into the genomes of thousands of bacterial species, 1,334 of which were previously unknown.

The data was analyzed using software developed by the project’s Italian partner, the University of Bologna. This program identifies species that behave similarly over time and groups them together.

“During the analyses, we could see that the bacteria in the sewage clustered into very different groups. We started to wonder why and how these groups were formed. At first, we thought that the clusters might represent microbes working together, but that was a dead end. Then we investigated whether some of the groups could consist of bacteria from human feces, and that’s when we hit the nail on the head,” says Patrick Munk.

The remaining groups were found to be bacteria of environmental origin, and one group present in wastewater treatment plants in all countries probably originates from biofilms growing on pipes leading to these plants.

Once researchers used analytical software to identify some groups, the task became easier.

“The principle is quite simple – some bacteria always come from humans, and bacteria that follow their sequences in the analysis are probably also from humans. In this way, we can identify groups of species that follow each other in time,” says Patrick Munk.

New method significantly increases success rate

Scientists have analyzed metagenomes before, but not as effectively as the new method.

“In this new study, we identified 1,334 previously unknown bacterial species in wastewater. Typically, when analyzing a metagenome consisting of 100 million small DNA fragments, we could only identify the origin of about 10% of the DNA. However, in this new study, we increased this to almost 70% of the DNA assigned to the species from which we recovered the genome,” says Patrick Munk.

The ability to detect new bacteria is crucial because these bacteria may carry previously unknown antimicrobial resistance genes, and this method has the potential to reveal new sources of antimicrobial resistance.

This is an observational study, in which the researchers worked with data based on the bacteria present in samples from untreated sewage, but they themselves did not adjust for any variables that might affect the frequency of specific bacteria. This introduces some uncertainty, and although many human-associated bacteria cluster together, they do not always do so. The next step is to create a synthetic data set, in which the researchers know which species of bacteria are present and actively change the conditions to observe the results.

“We don’t have a definitive success rate for this method yet, but it’s clear we’re onto something significant. We need to continue optimizing the method to improve its accuracy,” says Patrick Munk.

What is a metagenome?

All living organisms have genetic material (a genome) made of DNA. Sewage and other samples contain many different species of microbes, including bacteria and viruses. When you extract mixed DNA from these species, you don’t just have one genome, you have a metagenome. If the genome of each species is like a jigsaw puzzle, the metagenome is like a whole bunch of jumbled pieces. Metagenomes can answer questions about which organisms were present and how common they were, making them valuable tools for monitoring disease-causing bacteria and the genes that make them resistant to antibiotics. Millions of pieces of DNA are read from each sample, and many samples can be analyzed on a supercomputer.